AI Engineering Workflow Developer for T Cloud Public (m/f/d)
Core
Build, test, and maintain AI-assisted development workflows to accelerate code understanding, documentation, build triage, and handover preparation for cloud software and platform work packages.
Role type
Junior AI Engineering Workflow Developer
Builds
AI-assisted engineering utilities, prompt packs, repository analysis scripts, documentation helpers, and integration adapters
Domain
Enterprise Cloud Platforms + AI Development Tooling
Deliverable
production ML models | product features | infrastructure
Required skills
Python, API integration, scripting, developer tooling, documentation, testing, lightweight automation services
Preferred skills
AI coding assistants, prompt engineering, LLM APIs, local model experimentation, RAG-style development
Technologies
Cursor, Windsurf, Continue, Cline, Aider, Claude Code, VS Code extensions, DeepSeek Coder, Qwen, CodeGeeX, StarCoder, Code Llama, Mistral, FastAPI, notebooks, LangChain, LlamaIndex, GitLab/GitHub APIs, Jenkins APIs, Markdown, Git, Docker, Kubernetes, Helm, Linux shells
Responsibilities
Implement and maintain small AI-assisted engineering utilities for repository indexing, code summarization, dependency extraction, log parsing, and documentation generation; Support senior engineers in configuring AI development environments, testing prompts, comparing model outputs, and documenting repeatable SDLC usage patterns; Create scripts and lightweight services that connect Git repositories, CI/CD logs, issue trackers, documentation stores, and internal model endpoints; Test open-source, open-weight, and Chinese coding models in approved environments and document strengths, limitations, risks, and practical usage guidance; Participate in reviews with senior engineers to validate AI-generated outputs, correct inaccuracies, and improve workflow quality over time
Seniority
Junior, hands-on IC